Good argument for new paradigm concepts and a vision of "awareness-based, love-infused, presence-centered, evolutionary leadership” (but beyond green meme concepts :-)) drawing on Steiner, Campbell, Kegan, Torbert, Wilber, etc. The article gives a solid overview over qualities, concepts and practices that are emerging and gives a taste on what a new paradigm of leadership and development "in relationship to nature, community and meaning" could actually look like embodied, and nd most importantly, scaled up. AC

Executive Summary Bad decisions can often be traced back to the way the decisions were made–the alternatives were not clearly defined, the right information was not collected, the costs and benefits were not accurately weighed. But sometimes the fault lies not in the decision-making process but rather in the mind of the decision maker. The way the human brain works can sabotage the choices we make. In this article, first published in 1998, John Hammond, Ralph Keeney, and Howard Raiffa examine eight psychological traps that can affect the way we make business decisions. The anchoring trap leads us to give disproportionate weight to the first information we receive. The status quo trap biases us toward maintaining the current situation–even when better alternatives exist. The sunk-cost trap inclines us to perpetuate the mistakes of the past. The confirming-evidence trap leads us to seek out information supporting an existing predilection and to discount opposing information. The framing trap occurs when we misstate a problem, undermining the entire decision-making process. The overconfidence trap makes us overestimate the accuracy of our forecasts. The prudence trap leads us to be overcautious when we make estimates about uncertain events. And the recallability trap prompts us to give undue weight to recent, dramatic events. The best way to avoid all the traps is awareness–forewarned is forearmed. But executives can also take other simple steps to protect themselves and their organizations from these mental lapses. The authors describe what managers can do to ensure that their important business decisions are sound and reliable.

Through theoretical analysis, we show how a superorganism may react to stimulus variations according to psychophysical laws observed in humans and other animals. We investigate an empirically-motivated honeybee house-hunting model, which describes a value-sensitive decision process over potential nest-sites, at the level of the colony. In this study, we show how colony decision time increases with the number of available nests, in agreement with the Hick-Hyman law of psychophysics, and decreases with mean nest quality, in agreement with Piéron’s law. We also show that colony error rate depends on mean nest quality, and difference in quality, in agreement with Weber’s law. Psychophysical laws, particularly Weber’s law, have been found in diverse species, including unicellular organisms. Our theoretical results predict that superorganisms may also exhibit such behaviour, suggesting that these laws arise from fundamental mechanisms of information processing and decision-making. Finally, we propose a combined psychophysical law which unifies Hick-Hyman’s law and Piéron’s law, traditionally studied independently; this unified law makes predictions that can be empirically tested.

Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects’ assessment of the behavior of a simplified version of Reynolds’ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.

Computers that operate more like the human brain than computers—a field sometimes referred to as neuromorphic computing—have promised a new era of powerful computing.

While this all seems promising, one of the big shortcomings in neuromorphic computing has been that it doesn’t mimic the brain in a very important way. In the brain, for every neuron there are a thousand synapses—the electrical signal sent between the neurons of the brain. This poses a problem because a transistor only has a single terminal, hardly an accommodating architecture for multiplying signals.

Now researchers at Northwestern University, led by Mark Hersam, have developed a new device that combines memristors—two-terminal non-volatile memory devices based on resistance switching—with transistors to create what Hersam and his colleagues have dubbed a “memtransistor” that performs both memory storage and information processing.

This most recent research builds on work that Hersam and his team conducted back in 2015 in which the researchers developed a three-terminal, gate-tunable memristor that operated like a kind of synapse.

Northwestern University researchers have developed a new “smell virtual landscape” that enables the study of how smells engage the brain’s navigation system. The work demonstrates, for the first time, that the mammalian brain can form a map of its surroundings based solely on smells.

Using behavioral science to address adherence barriers is not new to sponsored patient support; in fact, behaviorally based programs have been around for years. However, the application of live, two-way support to identify barriers in real time and deploy specific messaging, resources and coaching support is on the rise.
Via Beeyond

Design defects in ballots, voter instructions, and voting machines contributed to the loss of several hundred thousand votes in the most recent national elections. This study outlines simple measures election officials can take to cure design defects and ensure every voter can cast a ballot that counts.

Neuroscientist Greg Gage takes sophisticated equipment used to study the brain out of graduate-level labs and brings them to middle- and high-school classrooms (and, sometimes, to the TED stage.) Prepare to be amazed as he hooks up the Mimosa pudica, a plant whose leaves close when touched, and the Venus flytrap to an EKG to show us how plants use electrical signals to convey information, prompt movement and even count.

Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing models which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.

Autonomous agents modelling other agents: A comprehensive survey and open problemsStefano V.Albrecht, PeterStone

Latest news and features on science issues that matter including earth, environment, and space. Get your science news from the most trusted source!

Researchers who want to predict the behavior of systems governed by quantum mechanics—an electron in an atom, say, or a photon of light traveling through space—typically turn to the Schrödinger equation. Devised by Austrian physicist Erwin Schrödinger in 1925, it describes subatomic particles and how they may display wavelike properties such as interference. It contains the essence of all that appears strange and counterintuitive about the quantum world. But it seems the Schrödinger equation is not confined to that realm. In a paper just published in Monthly Notices of the Royal Astronomical Society, planetary scientist Konstantin Batygin of the California Institute of Technology claims this equation can also be used to understand the emergence and behavior of self-gravitating astrophysical disks. That is, objects such as the rings of the worlds Saturn and Uranus or the halos of dust and gas that surround young stars and supply the raw material for the formation of a planetary system or even the accretion disks of debris spiraling into a black hole.

The British anthropologist’s pioneering research on human social behavior has shaped business theory, military planning, and social-network design. You famously posited that humans have the cognitive capacity to maintain about 150 stable social relationships. How have tools such as Facebook changed our capacity to handle social connections? Tight circles: Oxford professor Robin Dunbar argues that there is a limit on how many friends we really can have. Apparently not at all. It is important to remember that the 150 is just one layer in a series of layers of acquaintanceship within which we sit. Beyond the 150 are at least two further layers (one at 500 and one at 1,500), which correspond to acquaintances (people we have a nodding acquaintance with) and faces we recognize. All that seems to be happening when people add more than 150 friends on Facebook is that they simply dip into these normal higher layers. If you like, Facebook has muddied the waters by calling them all friends, but really they are not.

Most executives think of decision making as a singular event that occurs at a particular point in time. In reality, though, decision making is a process fraught with power plays, politics, personal nuances, and institutional history. Leaders who recognize this make far better decisions than those who persevere in the fantasy that decisions are events they alone control. That said, some decision-making processes are far more effective than others. Most often, participants use an advocacy process, possibly the least productive way to get things done. They view decision making as a contest, arguing passionately for their preferred solutions, presenting information selectively, withholding relevant conflicting data so they can make a convincing case, and standing firm against opposition. Much more powerful is an inquiry process, in which people consider a variety of options and work together to discover the best solution. Moving from advocacy to inquiry requires careful attention to three critical factors: fostering constructive, rather than personal, conflict; making sure everyone knows that their viewpoints are given serious consideration even if they are not ultimately accepted; and knowing when to bring deliberations to a close. The authors discuss in detail strategies for moving from an advocacy to an inquiry process, as well as for fostering productive conflict, true consideration, and timely closure. And they offer a framework for assessing the effectiveness of your process while you’re still in the middle of it. Decision making is a job that lies at the very heart of leadership and one that requires a genius for balance: the ability to embrace the divergence that may characterize early discussions and to forge the unity needed for effective implementation.

hen we make decisions, we make mistakes. We all know this from personal experience, of course. But just in case we didn’t, a seemingly unending stream of experimental evidence in recent years has documented the human penchant for error. This line of research—dubbed heuristics and biases, although you may be more familiar with its offshoot, behavioral economics—has become the dominant academic approach to understanding decisions. Its practitioners have had a major influence on business, government, and financial markets. Their books—Predictably Irrational; Thinking, Fast and Slow; and Nudge, to name three of the most important—have suffused popular culture. So far, so good. This research has been enormously informative and valuable. Our world, and our understanding of decision making, would be much poorer without it.

Computer-based swarm systems, aiming to replicate the flocking behavior of birds, were first introduced by Reynolds in 1987. In his initial work, Reynolds noted that while it was difficult to quantify the dynamics of the behavior from the model, observers of his model immediately recognized them as a representation of a natural flock. Considerable analysis has been conducted since then on quantifying the dynamics of flocking/swarming behavior. However, no systematic analysis has been conducted on human identification of swarming. In this paper, we assess subjects’ assessment of the behavior of a simplified version of Reynolds’ model. Factors that affect the identification of swarming are discussed and future applications of the resulting models are proposed. Differences in decision times for swarming-related questions asked during the study indicate that different brain mechanisms may be involved in different elements of the behavior assessment task. The relatively simple but finely tunable model used in this study provides a useful methodology for assessing individual human judgment of swarming behavior.

The idea of a hierarchical spatial organization of society lies at the core of seminal theories in human geography that have strongly influenced our understanding of social organization. Along the same line, the recent availability of large-scale human mobility and communication data has offered novel quantitative insights hinting at a strong geographical confinement of human interactions within neighboring regions, extending to local levels within countries. However, models of human interaction largely ignore this effect. Here, we analyze several country-wide networks of telephone calls - both mobile and landline - and in either case uncover a systematic decrease of communication induced by borders we identify as the missing variable in state-of-the-art models. Using this empirical evidence, we propose an alternative modeling framework that naturally stylizes the damping effect of borders. We show that this new notion substantially improves the predictive power of widely used interaction models. This increases our ability to understand, model and predict social activities and to plan the development of infrastructures across multiple scales.Empty description

Big Data, AI, and social media echo chambers can feel scary, but if harnessed correctly they can dramatically improve our quality of life. The potential for improvement comes first from better scientific understanding of our human minds and bodies, and second from a more open and shared understanding of society, government, and our day-to-day lives. The key to achieving these positive results is aggressive pursuit of a new, broad science of human life to unify the traditional and narrow sciences, and making data a trusted and safe resource for everyone. We are building such systems today, and are changing “business as usual” for governments around the world, as well as beginning to unify fragmented social and computational sciences.

Low voter turnout is a democratic problem that may be fixed with help from behavioral science. Here are a few insightful nudges that has proven to work. Last week the elections for the European Parliament was carried out in all countries. Though some considerations exist, a strong voter turnout is generally seen as desirable. However, the turnout for this particular vote has seen a decrease across Europe in latest elections, including last week’s elections. In light of this, we thought it was worth looking a bit into voting behaviour from a behavioural perspective.

The sound of running water (SRW) has been effectively used for toilet training during toddlerhood. However, the effect of SRW on voiding functions in adult males with lower urinary tract symptoms (LUTS) has not been evaluated. To determine the effect of SRW on urination in male patients with LUTS, multiple voiding parameters of uroflowmetry with postvoid residual urine (PVR) were assessed according to the presence of SRW played by a mobile application.

Definition of status quo bias, a concept from behavioral economics.Status quo bias is evident when people prefer things to stay the same by doing nothing (see also inertia) or by sticking with a decision made previously (Samuelson, & Zeckhauser, 1988). This may happen even when only small transition costs are involved and the importance of the decision is great. Field data from university health plan enrolments, for example, show a large disparity in health plan choices between new and existing enrollees that could not be explained by unchanging preferences. One particular plan with significantly more favorable premiums and deductibles had a growing market share among new employees but a significantly lower share among older enrollees. Samuelson and Zeckhauser note that status quo bias is consistent with loss aversion, and that it could be psychologically explained by previously made commitments and sunk cost thinking, cognitive dissonance, a need to feel in control and regret avoidance. The latter is based on Kahneman and Tversky’s observation that people feel greater regret for bad outcomes that result from new actions taken than for bad consequences that are the consequence of inaction (Kahneman & Tversky, 1982).

Few political and social occasions have been needing and taking advantage of behavioural insights as much as the period preceding political elections and the moment of the vote itself. 28722350_10215636544330017_1689889187_nIn fact, nowadays, two major problems obstruct the correct and useful functioning of the democratic ritual that voting represents: on the one hand, the constant lowering of voters’ turnout that modern democracy is currently facing, and, on the other hand, the ambiguity of the formulation of questions and answers on the actual voting ballot, which only leads citizens to get confused about how they should vote. The applications of nudges during political elections have been numerous and widespread in the last few years and to give an idea of the immense power that behavioural interventions can have in this field, the following paragraphs will propose a comparison between a disastrous case that was caused by poor application of behavioral insights versus some clever and winning examples of it.

What is artificial intelligence? Could unintended consequences arise from increased use of this technology? How will the role of humans change with AI? How will AI evolve in the next 10 years?

In this episode, Haley interviews leading Complex Systems Scientist, Professor of Computer Science at Portland State University, and external professor at the Santa Fe Institute, Melanie Mitchell. Professor Mitchell answers many profound questions about the field of artificial intelligence and gives specific examples of how this technology is being used today. She also provides some insights to help us navigate our relationship with AI as it becomes more popular in the coming years.

The emerging neurocomputational vision of humans as embodied, ecologically embedded, social agents—who shape and are shaped by their environment—offers a golden opportunity to revisit and revise ideas about the physical and information-theoretic underpinnings of life, mind, and consciousness itself. In particular, the active inference framework (AIF) makes it possible to bridge connections from computational neuroscience and robotics/AI to ecological psychology and phenomenology, revealing common underpinnings and overcoming key limitations. AIF opposes the mechanistic to the reductive, while staying fully grounded in a naturalistic and information-theoretic foundation, using the principle of free energy minimization. The latter provides a theoretical basis for a unified treatment of particles, organisms, and interactive machines, spanning from the inorganic to organic, non-life to life, and natural to artificial agents. We provide a brief introduction to AIF, then explore its implications for evolutionary theory, ecological psychology, embodied phenomenology, and robotics/AI research. We conclude the paper by considering implications for machine consciousness.

Despite the obvious advantage of simple life forms capable of fast replication, different levels of cognitive complexity have been achieved by living systems in terms of their potential to cope with environmental uncertainty. Against the inevitable cost associated with detecting environmental cues and responding to them in adaptive ways, we conjecture that the potential for predicting the environment can overcome the expenses associated with maintaining costly, complex structures. We present a minimal formal model grounded in information theory and selection, in which successive generations of agents are mapped into transmitters and receivers of a coded message. Our agents are guessing machines and their capacity to deal with environments of different complexity defines the conditions to sustain more complex agents.

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